Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

5-2025

Abstract

Maritime traffic management in busy ports faces growing challenges due to increased vessel traffic and complex waterway interactions. Strategies such as e-navigation by the International Maritime Organization aim to enhance navigation safety through traffic digitization. Maritime traffic simulation is essential for these systems, offering a virtual environment to model, analyze, and optimize traffic flows. Unlike road traffic, there are few simulators for maritime traffic, and they often lack realism and multi-ship interactions. In this paper, we (a) present ShipNaviSim, a data-driven maritime traffic simulator that utilizes a large-scale dataset over 2 years and electronic navigation charts to model vessel movements in Singapore Strait, one of the busiest ports in the world; (b) implement and evaluate different imitation learning algorithms such as behavior cloning to learn a policy that can accurately simulate real world vessel movements and multi-ship interactions; (c) develop vessel-specific metrics such as trajectory curvature, near miss rate, to validate the learned policy's alignment with human expert data. Extensive testing shows that our learned agents can behave like human experts, and thus can be used with the simulator for recommending routes for vessels in a hotspot region or generating diverse traffic scenarios to benchmark navigation systems.

Keywords

Imitation Learning; Maritime Traffic Simulation; Reinforcement Learning

Discipline

Artificial Intelligence and Robotics | Databases and Information Systems

Research Areas

Intelligent Systems and Optimization

Areas of Excellence

Sustainability

Publication

AAMAS '25: Proceedings of the 24th International Conference on Autonomous Agents and Multiagent Systems, Detroit, MI, USA, May 19-23

First Page

1641

Last Page

1649

ISBN

9798400714269

Identifier

10.5555/3709347.3743799

Publisher

ACM

City or Country

New York

Additional URL

https://doi.org/10.5555/3709347.3743799

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